仿人手软体机械手融合多模态信号，实现高效精准的物体与姿态识别。
Human-Inspired Soft Anthropomorphic Hand System for Neuromorphic Object and Pose Recognition Using Multimodal Signals
- 用脉冲神经网络处理触觉、本体感觉等多模态信号数据
- 物体识别准确率达97.14%，显著优于以往软体手研究
- 适合需要类人感知能力的机器人系统研发人员参考
人类本体感觉系统整合触觉、本体感觉和热觉等多种感官反馈，实现对环境的全面感知与有效交互。受此生物机制启发，我们提出一种集成多种传感器的仿生软体人手系统，模拟人体手掌的感官模态。该系统采用生物启发编码方案，将多模态传感数据转换为脉冲序列，通过脉冲神经网络（SNNs）实现高效处理。利用这些类脑信号，所提框架在不同姿态下的物体识别准确率达到97.14%，显著优于此前软体手相关研究。此外，我们引入一种新型微分神经元模型，通过捕捉动态热响应提升材料分类性能。结果表明，多模态感官融合具有显著优势，凸显了类脑方法在实现高效、鲁棒且类人感知方面的潜力。
原文摘要 · Abstract (English)
The human somatosensory system integrates multimodal sensory feedback, including tactile, proprioceptive, and thermal signals, to enable comprehensive perception and effective interaction with the environment. Inspired by the biological mechanism, we present a sensorized soft anthropomorphic hand equipped with diverse sensors designed to emulate the sensory modalities of the human hand. This system incorporates biologically inspired encoding schemes that convert multimodal sensory data into spike trains, enabling highly-efficient processing through Spiking Neural Networks (SNNs). By utilizing these neuromorphic signals, the proposed framework achieves 97.14% accuracy in object recognition across varying poses, significantly outperforming previous studies on soft hands. Additionally, we introduce a novel differentiator neuron model to enhance material classification by capturing dynamic thermal responses. Our results demonstrate the benefits of multimodal sensory fusion and highlight the potential of neuromorphic approaches for achieving efficient, robust, and human-like perception in robotic systems.
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